Since the graph neural networks (GNN) show great power recently, many approaches [1]}, [2]}, [3]}, [4]}, [5]} adopt GNN for the association, rather than using conventional graph models based on optimization. Specifically, [2]} exploits the classical network flow formulation of MOT to define a fully differentiable framework based on message passing networks. [3]} presents an adaptive graph neural network to fuse locations, appearance, and historical information for MOT. [4]} proposes a novel feature interaction mechanism based on the GNN to learn the interaction among objects. [5]} presents a novel learnable graph matching method to address association issues in MOT. [10]} proposes a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph. The existing methods show the effectiveness of employing GNN to MOT.
Moreover, GNN also shows power in other related vision tasks, such as human action recognition [11]}, visual question answering [12]} and single object tracking [13]}.
